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Detection of Zero-Day Security Threat Using Machine Learning

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Overview

This project and publication focus on developing a machine learning-based approach to detect zero-day security threats, which are vulnerabilities unknown to security personnel and often exploited by hackers. Traditional security measures struggle with zero-day attacks due to the lack of prior knowledge or signature information, making this research crucial for enhancing cybersecurity.

Key Contributions

  1. Novel Security Solution: Developed an Intrusion Detection System (IDS) leveraging Machine Learning (ML) and Deep Learning (DL) to identify zero-day vulnerabilities in real-time.
  2. Hybrid Technologies: Implemented a hybrid approach combining static and dynamic analysis to enhance the detection accuracy and reduce false negatives.
  3. Machine Learning Algorithms: Utilized Random Forest, Naïve Bayes, and Decision Tree algorithms to classify and detect potential security threats.

Project Highlights

1. Data Collection and Preprocessing:

  • Datasets: Sourced and prepared datasets from Kaggle containing samples of known malware.
  • Data Preparation: Handled missing values through mean and median calculations, and normalized data using label encoding techniques.

2. Model Architecture:

  • System Architecture: The project is divided into four phases: Malware Data Sets (MDS) collection, MDS analysis, learning algorithms implementation, and detection/classification of attacks.
  • Learning Algorithms: Applied Random Forest, Naïve Bayes, and Decision Tree algorithms to correlate relationships between malware characteristics and potential vulnerabilities.

Core Algorithms and Code Snippets

Random Forest Implementation:

python
from sklearn.ensemble import RandomForestClassifier

# Training the Random Forest model
rf_model = RandomForestClassifier(n_estimators=100)
rf_model.fit(X_train, y_train)

# Predicting on the test set
predictions = rf_model.predict(X_test)

Naive Bayes Implementation:

python
from sklearn.naive_bayes import GaussianNB

# Training the Naive Bayes model
nb_model = GaussianNB()
nb_model.fit(X_train, y_train)

# Predicting on the test set
predictions = nb_model.predict(X_test)

Decision Tree Implementation:

python
from sklearn.tree import DecisionTreeClassifier

# Training the Decision Tree model
dt_model = DecisionTreeClassifier()
dt_model.fit(X_train, y_train)

# Predicting on the test set
predictions = dt_model.predict(X_test)

Experimental Results

  1. Performance Metrics: Achieved high accuracy in detecting zero-day threats with the proposed hybrid model. The graphical results demonstrated the effectiveness of Random Forest, Naive Bayes, and Decision Tree algorithms in reducing false negatives.
  2. User Interface: Developed a user-friendly interface for real-time threat detection, providing visual feedback on the type of attack and its severity.

Conclusion and Future Work

  • Significance: This work demonstrates the potential of machine learning in enhancing the detection of zero-day vulnerabilities, contributing to more robust cybersecurity practices.
  • Future Directions: The next steps involve refining the model with more complex datasets and exploring advanced deep learning techniques for even higher detection accuracy.

View Publication

Link: View Full Publication